A Wavelet Transform and self-supervised learning-based framework for bearing fault diagnosis with limited labeled data

TL;DR

This paper presents a novel wavelet transform and self-supervised learning framework for bearing fault diagnosis that achieves high accuracy with only 1% labeled data, using vision transformers and self-distillation techniques.

Contribution

It introduces a combined wavelet transform and self-supervised learning approach utilizing Vision Transformer and DINO for fault diagnosis with limited labeled data.

Findings

Over 90% diagnosis accuracy with 1% labeled data

Effective feature extraction with Vision Transformer

Outperforms other self-supervised methods

Abstract

Traditional supervised bearing fault diagnosis methods rely on massive labelled data, yet annotations may be very time-consuming or infeasible. The fault diagnosis approach that utilizes limited labelled data is becoming increasingly popular. In this paper, a Wavelet Transform (WT) and self-supervised learning-based bearing fault diagnosis framework is proposed to address the lack of supervised samples issue. Adopting the WT and cubic spline interpolation technique, original measured vibration signals are converted to the time-frequency maps (TFMs) with a fixed scale as inputs. The Vision Transformer (ViT) is employed as the encoder for feature extraction, and the self-distillation with no labels (DINO) algorithm is introduced in the proposed framework for self-supervised learning with limited labelled data and sufficient unlabeled data. Two rolling bearing fault datasets are used for validations. In the case of both datasets only containing 1% labelled samples, utilizing the feature vectors extracted by the trained encoder without fine-tuning, over 90\% average diagnosis accuracy can be obtained based on the simple K-Nearest Neighbor (KNN) classifier. Furthermore, the superiority of the proposed method is demonstrated in comparison with other self-supervised fault diagnosis methods.